This handbook is not easy to classify. It is neither a statistics textbook nor a software manual. It uses a number of datasets from the earlier Handbook of Small Datasets (Hand et al.) and may have been partly inspired by that volume. In a private communication, one of the authors of the earlier work expressed a hope that it would be followed by example analyses of the data. Going even further back in time we have The Minitab Handbook which did for Minitab what this volume attempts to do for R. That is to say, both provide an extensive selection of real data analyzed with the software du jour. In addition to the difference in software, there is a difference in level, The statistical topics covered in the Minitab book comprise the most elementary 30% of the topics covered in the book at hand.

Viewed as a collection of worked examples, this book has much to recommend it. Each chapter (there are 18) addresses a specific technique. Chapters open with two to a few datasets printed with a minimal explanation of context, followed by an analysis of each dataset in turn. A typical analysis might involve a standard approach modified by a tweak or two that illustrate what R can do. Usually one peculiarity is uncovered and the analysis takes one step in further analyzing the peculiarity. Many of the analyses then end here abruptly, which left this reader hanging. There seemed to be no attempt to look for other peculiarities nor to wrap up the analysis with an overall summary of what was learned from the analysis. Still the examples provide a wide variety of partial analyses and the datasets cover a diversity of fields of study.

The book is less successful in explaining the statistical techniques or the use of R. Often there is little explanation of R beyond “here is the code and here is the output.” While an occasional command is explained in detail, readers not already well-versed in R will find themselves constantly looking up details elsewhere in order to see just what the code says. The statistical techniques get very short summaries. These are often quite good given their brevity, at times even elegant, but they seem at much too high a level of abstraction to be of much use in following the examples. All the Greek letters and summation signs do not help the reader figure out what that –1.467 in the R output means.

This handbook is unusually free of the sort of errors spell checkers do not find. There are some unsolved problems with page layout. Often there are too many pages separating the analysis from the data description and the R output. This is not an easy problem but it is one that has been addressed much more successfully, e.g., in the works on graphics by Edward Tufte.

Perhaps the best audience for this work would be people teaching some of this material to upper division or graduate students. For them it could provide a number of data examples and sketches of analyses. The students might be asked to complete the analyses. The teacher would already know the statistics and ideally a good deal of R as well.

After a few years in industry, Robert W. Hayden (bob@statland.org) taught mathematics at colleges and universities for 32 years and statistics for 20 years. In 2005 he retired from full-time classroom work. He now teaches statistics online at statistics.com and does summer workshops for high school teachers of Advanced Placement Statistics. He contributed the chapter on evaluating introductory statistics textbooks to the MAA's Teaching Statistics.